mlsanityData to 14 Oct 2026

Autonomous AI Agents for Real-Time Affordable Housing Site Selection: Multi-Objective Reinforcement Learning Under Regulatory Constraints

arXiv 2602.03940 ↗First posted 3 Feb 2026cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

Affordable housing shortages affect billions, while land scarcity and regulations make site selection slow. We present AURA (Autonomous Urban Resource Allocator), a hierarchical multi-agent reinforcement learning system for real-time affordable housing site selection under hard regulatory constraints (QCT, DDA, LIHTC). We model the task as a constrained multi-objective Markov decision process optimizing accessibility, environmental impact, construction cost, and social equity while enforcing feasibility. AURA uses a regulatory-aware state encoding 127 federal and local constraints, Pareto-constrained policy gradients with feasibility guarantees, and reward decomposition separating immediate costs from long-term social outcomes. On datasets from 8 U.S. metros (47,392 candidate parcels), AURA attains 94.3% regulatory compliance and improves Pareto hypervolume by 37.2% over strong baselines. In a New York City 2026 case study, it reduces selection time from 18 months to 72 hours and identifies 23% more viable sites; chosen sites have 31% better transit access and 19% lower environmental impact than expert picks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 33 references that are in the map. First posted on arXiv 3 Feb 2026.

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8 of 33 references.

References · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Graph Attention Networkscited by 8,923
  3. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
  4. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  5. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,439
  6. 2018Data-Efficient Hierarchical Reinforcement Learningcited by 455
  7. 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 256
  8. 2018Why Is My Classifier Discriminatory?cited by 143

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